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@STRING{ams = {American Mathematical Society}}

@STRING{ap = {Academic Press}}

@STRING{cgip = {Computer Graphics and Image Processing}}

@STRING{cs-unc = {Department of Computer Science, University of North Carolina at Chapel
	Hill}}

@STRING{cvgip = {Computer Vision, Graphics, and Image Processing}}

@STRING{cvgip-iu = {Computer Vision, Graphics, and Image Processing: Image Understanding}}

@STRING{ieeetranscom = {{IEEE} Transactions on Communications}}

@STRING{josa = {Journal of the Optical Society of {A}merica}}

@STRING{josa-a = {Journal of the Optical Society of {A}merica {A}}}

@STRING{lea = {Lawrence Erlbaum Associates}}

@STRING{lea-addr = {Hillsdale, New Jersey}}

@STRING{mit = {The MIT Press}}

@STRING{mit-addr = {Cambridge, MA}}

@STRING{pami = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence}}

@STRING{ph = {Prentice Hall}}

@STRING{prletters = {Pattern Recognition Letters}}

@STRING{procnas = {Proc. Natl. Acad. Sci. USA}}

@STRING{psychrev = {Psychological Review}}

@STRING{sciam = {Scientific American}}

@STRING{spatialvis = {Spatial Vision}}

@STRING{springer = {Springer-Verlag}}

@STRING{tins = {Trends in Neuroscience ({TINS})}}

@STRING{unc-ch = {University of North Carolina at Chapel Hill}}

@STRING{vr = {Vision Research}}

@INPROCEEDINGS{Bauer2011c,
  author = {Bauer, Stefan and Nolte, Lutz-Peter and Reyes, Mauricio},
  title = {{Skull-stripping for Tumor-bearing Brain Images}},
  booktitle = {Annual Meeting of the Swiss Society for Biomedical Engineering},
  year = {2011},
  editor = {B\"{u}chler, Philippe and Ferguson, Stephen},
  pages = {2},
  address = {Bern},
  month = apr,
  publisher = {SSBE},
  note = {arXiv:1204.0357},
  abstract = {Skull-stripping separates the skull region of the head from the soft
	brain tissues. In many cases of brain image analysis, this is an
	essential preprocessing step in order to improve the final result.
	This is true for both registration and segmentation tasks. In fact,
	skull-stripping of magnetic resonance images (MRI) is a well-studied
	problem with numerous publications in recent years. Many different
	algorithms have been proposed, a summary and comparison of which
	can be found in [Fennema-Notestine, 2006]. Despite the abundance
	of approaches, we discovered that the algorithms which had been suggested
	so far, perform poorly when dealing with tumor-bearing brain images.
	This is mostly due to additional difficulties in separating the brain
	from the skull in this case, especially when the lesion is located
	very close to the skull border. Additionally, images acquired according
	to standard clinical protocols, often exhibit anisotropic resolution
	and only partial coverage, which further complicates the task. Therefore,
	we developed a method which is dedicated to skull-stripping for clinically
	acquired tumor-bearing brain images.},
  archiveprefix = {arXiv},
  arxivid = {1204.0357},
  eprint = {1204.0357},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Publications/conferences/SSBE2011/skullStripping/bauer-reyes-SSBE2011.pdf:pdf},
  keywords = {Brain tumor,MRI,Segmentation,Skull-stripping},
  owner = {Stefan},
  timestamp = {2012.04.18},
  url = {http://www.istb.unibe.ch/content/ssbe\_2011/ http://arxiv.org/abs/1204.0357}
}

@ARTICLE{Carass2011,
  author = {Carass, Aaron and Cuzzocreo, Jennifer and Wheeler, M Bryan and Bazin,
	Pierre-Louis and Resnick, Susan M and Prince, Jerry L},
  title = {{Simple paradigm for extra-cerebral tissue removal: Algorithm and
	analysis.}},
  journal = {NeuroImage},
  year = {2011},
  volume = {56},
  pages = {1982--1992},
  number = {4},
  month = mar,
  abstract = {Extraction of the brain-i.e. cerebrum, cerebellum, and brain stem-from
	T1-weighted structural magnetic resonance images is an important
	initial step in neuroimage analysis. Although automatic algorithms
	are available, their inconsistent handling of the cortical mantle
	often requires manual interaction, thereby reducing their effectiveness.
	This paper presents a fully automated brain extraction algorithm
	that incorporates elastic registration, tissue segmentation, and
	morphological techniques which are combined by a watershed principle,
	while paying special attention to the preservation of the boundary
	between the gray matter and the cerebrospinal fluid. The approach
	was evaluated by comparison to a manual rater, and compared to several
	other leading algorithms on a publically available data set of brain
	images using the Dice coefficient and containment index as performance
	metrics. The qualitative and quantitative impact of this initial
	step on subsequent cortical surface generation is also presented.
	Our experiments demonstrate that our approach is quantitatively better
	than six other leading algorithms (with statistical significance
	on modern T1-weighted MR data). We also validated the robustness
	of the algorithm on a very large data set of over one thousand subjects,
	and showed that it can replace an experienced manual rater as preprocessing
	for a cortical surface extraction algorithm with statistically insignificant
	differences in cortical surface position.},
  doi = {10.1016/j.neuroimage.2011.03.045},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/brain\_segmentation/carass2011.pdf:pdf},
  issn = {1095-9572},
  keywords = {Brain extraction,Medical image processing,Segmentation,Skull stripping,Watershed
	principle},
  owner = {Stefan},
  pmid = {21458576},
  publisher = {Elsevier Inc.},
  timestamp = {2012.04.18},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/21458576}
}

@ARTICLE{Cocosco1997,
  author = {Cocosco, C.A. and Kollokian, V. and Kwan, R.S. and Evans, A.C.},
  title = {{BrainWeb: Online interface to a 3D MRI simulated brain database}},
  journal = {Neuroimage},
  year = {1997},
  volume = {5},
  number = {4},
  owner = {Stefan},
  timestamp = {2012.04.19}
}

@ARTICLE{Eskildsen2011,
  author = {Eskildsen, Simon F and Coup\'{e}, Pierrick and Fonov, Vladimir and
	Manj\'{o}n, Jos\'{e} V and Leung, Kelvin K and Guizard, Nicolas and
	Wassef, Shafik N and Ostergaard, Lasse Riis and Collins, D Louis},
  title = {{BEaST: Brain extraction based on nonlocal segmentation technique.}},
  journal = {NeuroImage},
  year = {2011},
  volume = {59},
  pages = {2362--2373},
  number = {3},
  month = sep,
  abstract = {Brain extraction is an important step in the analysis of brain images.
	The variability in brain morphology and the difference in intensity
	characteristics due to imaging sequences make the development of
	a general purpose brain extraction algorithm challenging. To address
	this issue, we propose a new robust method (BEaST) dedicated to produce
	consistent and accurate brain extraction. This method is based on
	nonlocal segmentation embedded in a multi-resolution framework. A
	library of 80 priors is semi-automatically constructed from the NIH-sponsored
	MRI study of normal brain development, the International Consortium
	for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative
	databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053
	was obtained when performing leave-one-out cross validation selecting
	only 20 priors from the library. Validation using the online Segmentation
	Validation Engine resulted in a top ranking position with a mean
	Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated
	on all baseline ADNI data, resulting in a very low failure rate.
	The segmentation accuracy of the method is better than two widely
	used publicly available methods and recent state-of-the-art hybrid
	approaches. BEaST provides results comparable to a recent label fusion
	approach, while being 40 times faster and requiring a much smaller
	library of priors.},
  doi = {10.1016/j.neuroimage.2011.09.012},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/brain\_segmentation/eskildsen2011.pdf:pdf},
  issn = {1095-9572},
  keywords = {BET,Brain extraction,MRI,Multi-resolution,Patch-based segmentation,Skull
	stripping},
  owner = {Stefan},
  pmid = {21945694},
  publisher = {Elsevier Inc.},
  timestamp = {2012.04.18},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/21945694}
}

@ARTICLE{Fennema-Notestine2006,
  author = {Fennema-Notestine, Christine and Ozyurt, I Burak and Clark, Camellia
	P and Morris, Shaunna and Bischoff-Grethe, Amanda and Bondi, Mark
	W and Jernigan, Terry L and Fischl, Bruce and Segonne, Florent and
	Shattuck, David W and Leahy, Richard M and Rex, David E and Toga,
	Arthur W and Zou, Kelly H and Brown, Gregory G},
  title = {{Quantitative evaluation of automated skull-stripping methods applied
	to contemporary and legacy images: effects of diagnosis, bias correction,
	and slice location.}},
  journal = {Human brain mapping},
  year = {2006},
  volume = {27},
  pages = {99--113},
  number = {2},
  month = feb,
  abstract = {Performance of automated methods to isolate brain from nonbrain tissues
	in magnetic resonance (MR) structural images may be influenced by
	MR signal inhomogeneities, type of MR image set, regional anatomy,
	and age and diagnosis of subjects studied. The present study compared
	the performance of four methods: Brain Extraction Tool (BET; Smith
	[2002]: Hum Brain Mapp 17:143-155); 3dIntracranial (Ward [1999] Milwaukee:
	Biophysics Research Institute, Medical College of Wisconsin; in AFNI);
	a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage
	22:1060-1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor
	and Leahy [1997] IEEE Trans Med Imag 16:41-54; Shattuck et al. [2001]
	Neuroimage 13:856-876) to manually stripped images. The methods were
	applied to uncorrected and bias-corrected datasets; Legacy and Contemporary
	T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer's,
	young and elderly control). To provide a criterion for outcome assessment,
	two experts manually stripped six sagittal sections for each dataset
	in locations where brain and nonbrain tissue are difficult to distinguish.
	Methods were compared on Jaccard similarity coefficients, Hausdorff
	distances, and an Expectation-Maximization algorithm. Methods tended
	to perform better on contemporary datasets; bias correction did not
	significantly improve method performance. Mesial sections were most
	difficult for all methods. Although AD image sets were most difficult
	to strip, HWA and BSE were more robust across diagnostic groups compared
	with 3dIntracranial and BET. With respect to specificity, BSE tended
	to perform best across all groups, whereas HWA was more sensitive
	than other methods. The results of this study may direct users towards
	a method appropriate to their T1-weighted datasets and improve the
	efficiency of processing for large, multisite neuroimaging studies.},
  doi = {10.1002/hbm.20161},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/notestine06.pdf:pdf},
  issn = {1065-9471},
  keywords = {Adult,Age Factors,Aged,Algorithms,Brain,Brain Diseases,Brain Diseases:
	radiography,Brain: radiography,Computer-Assisted,Computer-Assisted:
	methods,Humans,Image Processing,Magnetic Resonance Imaging,Middle
	Aged,Sensitivity and Specificity,Software},
  owner = {Stefan},
  pmid = {15986433},
  publisher = {John Wiley \& Sons},
  timestamp = {2012.04.18},
  url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2408865\&tool=pmcentrez\&rendertype=abstract}
}

@INPROCEEDINGS{Hahn2000,
  author = {Hahn, H. and Peitgen, H.O.},
  title = {{The skull stripping problem in MRI solved by a single 3D watershed
	transform}},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention–MICCAI
	2000},
  year = {2000},
  pages = {129--145},
  publisher = {Springer},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/hahn00.pdf:pdf},
  keywords = {3d watershed transform,neurological image processing,pre-flooding,skull
	stripping,whole brain segmentation},
  owner = {Stefan},
  timestamp = {2012.04.18},
  url = {http://www.springerlink.com/index/DEM79E6JGDKW3CW9.pdf}
}

@ARTICLE{Hartley2006,
  author = {Hartley, SW and Scher, AI and Korf, ESC and White, LR and Launer,
	LJ},
  title = {{Analysis and validation of automated skull stripping tools: a validation
	study based on 296 MR images from the Honolulu Asia aging study}},
  journal = {Neuroimage},
  year = {2006},
  volume = {30},
  pages = {1179--1186},
  number = {4},
  doi = {10.1016/j.neuroimage.2005.10.043},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/hartley06.pdf:pdf},
  issn = {1053-8119},
  owner = {Stefan},
  publisher = {Elsevier},
  timestamp = {2012.04.18},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811905024407}
}

@BOOK{Ibanez2003,
  title = {{The ITK software guide}},
  publisher = {Citeseer},
  year = {2003},
  author = {Ibanez, L. and Schroeder, Will and Ng, L. and Cates, J. and Others},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/manuals/ItkSoftwareGuide-2.4.0.pdf:pdf},
  owner = {Stefan},
  timestamp = {2012.04.18},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.3166}
}

@ARTICLE{Iglesias2011,
  author = {Iglesias, Juan Eugenio and Liu, Cheng-Yi and Thompson, Paul M and
	Tu, Zhuowen},
  title = {{Robust brain extraction across datasets and comparison with publicly
	available methods.}},
  journal = {IEEE transactions on medical imaging},
  year = {2011},
  volume = {30},
  pages = {1617--34},
  number = {9},
  month = sep,
  abstract = {Automatic whole-brain extraction from magnetic resonance images (MRI),
	also known as skull stripping, is a key component in most neuroimage
	pipelines. As the first element in the chain, its robustness is critical
	for the overall performance of the system. Many skull stripping methods
	have been proposed, but the problem is not considered to be completely
	solved yet. Many systems in the literature have good performance
	on certain datasets (mostly the datasets they were trained/tuned
	on), but fail to produce satisfactory results when the acquisition
	conditions or study populations are different. In this paper we introduce
	a robust, learning-based brain extraction system (ROBEX). The method
	combines a discriminative and a generative model to achieve the final
	result. The discriminative model is a Random Forest classifier trained
	to detect the brain boundary; the generative model is a point distribution
	model that ensures that the result is plausible. When a new image
	is presented to the system, the generative model is explored to find
	the contour with highest likelihood according to the discriminative
	model. Because the target shape is in general not perfectly represented
	by the generative model, the contour is refined using graph cuts
	to obtain the final segmentation. Both models were trained using
	92 scans from a proprietary dataset but they achieve a high degree
	of robustness on a variety of other datasets. ROBEX was compared
	with six other popular, publicly available methods (BET, BSE, FreeSurfer,
	AFNI, BridgeBurner, and GCUT) on three publicly available datasets
	(IBSR, LPBA40, and OASIS, 137 scans in total) that include a wide
	range of acquisition hardware and a highly variable population (different
	age groups, healthy/diseased). The results show that ROBEX provides
	significantly improved performance measures for almost every method/dataset
	combination.},
  doi = {10.1109/TMI.2011.2138152},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/iglesias2011.pdf:pdf},
  issn = {1558-0062},
  owner = {Stefan},
  pmid = {21880566},
  timestamp = {2012.04.18},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/21880566}
}

@ARTICLE{Lee2003,
  author = {Lee, J},
  title = {{Evaluation of automated and semi-automated skull-stripping algorithms
	using similarity index and segmentation error}},
  journal = {Computers in Biology and Medicine},
  year = {2003},
  volume = {33},
  pages = {495--507},
  number = {6},
  month = nov,
  doi = {10.1016/S0010-4825(03)00022-2},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/lee03.pdf:pdf},
  issn = {00104825},
  keywords = {false negative rate,false positive rate,inter-rater reliability,similarity
	index,simulated t1-weighted mri,skull-stripping},
  owner = {Stefan},
  timestamp = {2012.04.18},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0010482503000222}
}

@ARTICLE{Segonne2004,
  author = {S\'{e}gonne, F. and Dale, A.M. and Busa, E and Glessner, M and Salat,
	D and Hahn, H.K. and Fischl, B},
  title = {{A hybrid approach to the skull stripping problem in MRI}},
  journal = {Neuroimage},
  year = {2004},
  volume = {22},
  pages = {1060--1075},
  number = {3},
  doi = {10.1016/j.neuroimage.2004.03.032},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/segonne04.pdf:pdf},
  issn = {1053-8119},
  keywords = {atlas-based segmentation,brain segmentation,construction of detailed
	head,data with eeg and,generate spatiotem-,meg sensor information
	to,models that can be,skull stripping,template deformation,used to
	fuse mri,watershed transformation},
  owner = {Stefan},
  publisher = {Elsevier},
  timestamp = {2012.04.18},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811904001880}
}

@ARTICLE{Sadananthan2010,
  author = {Sadananthan, S.A. and Zheng, Weili and Chee, M.W.L. and Zagorodnov,
	Vitali},
  title = {{Skull stripping using graph cuts}},
  journal = {NeuroImage},
  year = {2010},
  volume = {49},
  pages = {225--239},
  number = {1},
  doi = {10.1016/j.neuroimage.2009.08.050},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/sadananthan10.pdf:pdf},
  issn = {1053-8119},
  owner = {Stefan},
  publisher = {Elsevier},
  timestamp = {2012.04.18},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811909009604}
}

@ARTICLE{Sandor1997,
  author = {Sandor, S and Leahy, R},
  title = {{Surface-based labeling of cortical anatomy using a deformable atlas.}},
  journal = {IEEE transactions on medical imaging},
  year = {1997},
  volume = {16},
  pages = {41--54},
  number = {1},
  month = feb,
  abstract = {We describe a computerized method to automatically find and label
	the cortical surface in three-dimensional (3-D) magnetic resonance
	(MR) brain images. The approach we take is to model a prelabeled
	brain atlas as a physical object and give it elastic properties,
	allowing it to warp itself onto regions in a preprocessed image.
	Preprocessing consists of boundary-finding and a morphological procedure
	which automatically extracts the brain and sulci from an MR image
	and provides a smoothed representation of the brain surface to which
	the deformable model can rapidly converge. Our deformable models
	are energy-minimizing elastic surfaces that can accurately locate
	image features. The models are parameterized with 3-D bicubic B-spline
	surfaces. We design the energy function such that cortical fissure
	(sulci) points on the model are attracted to fissure points on the
	image and the remaining model points are attracted to the brain surface.
	A conjugate gradient method minimizes the energy function, allowing
	the model to automatically converge to the smoothed brain surface.
	Finally, labels are propagated from the deformed atlas onto the high-resolution
	brain surface.},
  doi = {10.1109/42.552054},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/brain\_segmentation/sandor1997.pdf:pdf},
  issn = {0278-0062},
  keywords = {Algorithms,Anatomy, Artistic,Brain,Brain: anatomy \& histology,Cerebral
	Cortex,Cerebral Cortex: anatomy \& histology,Computer Simulation,Elasticity,Frontal
	Lobe,Frontal Lobe: anatomy \& histology,Humans,Image Enhancement,Image
	Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted:
	methods,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted:
	methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Medical
	Illustration,Occipital Lobe,Occipital Lobe: anatomy \& histology,Parietal
	Lobe,Parietal Lobe: anatomy \& histology,Software,Temporal Lobe,Temporal
	Lobe: anatomy \& histology},
  owner = {Stefan},
  pmid = {9050407},
  timestamp = {2012.04.18},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/9050407}
}

@ARTICLE{Shattuck2009,
  author = {Shattuck, David W and Prasad, Gautam and Mirza, Mubeena and Narr,
	Katherine L and Toga, Arthur W},
  title = {{Online resource for validation of brain segmentation methods.}},
  journal = {NeuroImage},
  year = {2009},
  volume = {45},
  pages = {431--9},
  number = {2},
  month = apr,
  abstract = {One key issue that must be addressed during the development of image
	segmentation algorithms is the accuracy of the results they produce.
	Algorithm developers require this so they can see where methods need
	to be improved and see how new developments compare with existing
	ones. Users of algorithms also need to understand the characteristics
	of algorithms when they select and apply them to their neuroimaging
	analysis applications. Many metrics have been proposed to characterize
	error and success rates in segmentation, and several datasets have
	also been made public for evaluation. Still, the methodologies used
	in analyzing and reporting these results vary from study to study,
	so even when studies use the same metrics their numerical results
	may not necessarily be directly comparable. To address this problem,
	we developed a web-based resource for evaluating the performance
	of skull-stripping in T1-weighted MRI. The resource provides both
	the data to be segmented and an online application that performs
	a validation study on the data. Users may download the test dataset,
	segment it using whichever method they wish to assess, and upload
	their segmentation results to the server. The server computes a series
	of metrics, displays a detailed report of the validation results,
	and archives these for future browsing and analysis. We applied this
	framework to the evaluation of 3 popular skull-stripping algorithms--the
	Brain Extraction Tool [Smith, S.M., 2002. Fast robust automated brain
	extraction. Hum. Brain Mapp. 17 (3),143-155 (Nov)], the Hybrid Watershed
	Algorithm [S\'{e}gonne, F., Dale, A.M., Busa, E., Glessner, M., Salat,
	D., Hahn, H.K., Fischl, B., 2004. A hybrid approach to the skull
	stripping problem in MRI. NeuroImage 22 (3), 1060-1075 (Jul)], and
	the Brain Surface Extractor [Shattuck, D.W., Sandor-Leahy, S.R.,
	Schaper, K.A., Rottenberg, D.A., Leahy, R.M., 2001. Magnetic resonance
	image tissue classification using a partial volume model. NeuroImage
	13 (5), 856-876 (May) under several different program settings. Our
	results show that with proper parameter selection, all 3 algorithms
	can achieve satisfactory skull-stripping on the test data.},
  doi = {10.1016/j.neuroimage.2008.10.066},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/brain\_segmentation/shattuck08.pdf:pdf},
  issn = {1095-9572},
  keywords = {Adult,Algorithms,Computer Simulation,Database Management Systems,Databases,
	Factual,Female,Humans,Image Enhancement,Image Enhancement: methods,Image
	Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted:
	methods,Information Dissemination,Information Dissemination: methods,Internet,Magnetic
	Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Models,
	Anatomic,Pattern Recognition, Automated,Pattern Recognition, Automated:
	methods,Reproducibility of Results,Sensitivity and Specificity},
  owner = {Stefan},
  pmid = {19073267},
  publisher = {Elsevier Inc.},
  timestamp = {2012.04.18},
  url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2757629\&tool=pmcentrez\&rendertype=abstract}
}

@INPROCEEDINGS{Shi2011,
  author = {Shi, Feng and Wang, Li and Gilmore, J and Lin, Weili and Shen, Dinggang},
  title = {{Learning-Based Meta-Algorithm for MRI Brain Extraction}},
  booktitle = {MICCAI - Medical Image Computing and Computer Assisted Interventions},
  year = {2011},
  editor = {Fichtinger, Gabor and Martel, Anne and Peters, Terry},
  pages = {313--321},
  address = {Toronto},
  doi = {10.1007/978-3-642-23626-6\_39},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/shi2011.pdf:pdf},
  owner = {Stefan},
  timestamp = {2012.04.18},
  url = {http://www.springerlink.com/index/5U91163835492036.pdf}
}

@ARTICLE{Smith2002a,
  author = {Smith, Stephen M},
  title = {{Fast robust automated brain extraction.}},
  journal = {Human brain mapping},
  year = {2002a},
  volume = {17},
  pages = {143--55},
  number = {3},
  month = nov,
  abstract = {An automated method for segmenting magnetic resonance head images
	into brain and non-brain has been developed. It is very robust and
	accurate and has been tested on thousands of data sets from a wide
	variety of scanners and taken with a wide variety of MR sequences.
	The method, Brain Extraction Tool (BET), uses a deformable model
	that evolves to fit the brain's surface by the application of a set
	of locally adaptive model forces. The method is very fast and requires
	no preregistration or other pre-processing before being applied.
	We describe the new method and give examples of results and the results
	of extensive quantitative testing against "gold-standard" hand segmentations,
	and two other popular automated methods.},
  doi = {10.1002/hbm.10062},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/brain\_segmentation/smith2002.pdf:pdf},
  issn = {1065-9471},
  keywords = {Algorithms,Animals,Brain,Brain: anatomy \& histology,Brain: physiology,Humans,Magnetic
	Resonance Imaging,Magnetic Resonance Imaging: instrumentation,Magnetic
	Resonance Imaging: methods},
  owner = {Stefan},
  pmid = {12391568},
  timestamp = {2012.04.18},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/12391568}
}

@INPROCEEDINGS{Speier2011,
  author = {Speier, William and Iglesias, J and El-Kara, L and Tu, Zhuowen and
	Arnold, Corey},
  title = {{Robust Skull Stripping of Clinical Glioblastoma Multiforme Data}},
  booktitle = {MICCAI - Medical Image Computing and Computer Assisted Interventions},
  year = {2011},
  editor = {Fichtinger, Gabor and Martel, Anne and Peters},
  pages = {659--666},
  address = {Toronto},
  publisher = {Springer LNCS},
  doi = {10.1007/978-3-642-23626-6\_81},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/speier2011.pdf:pdf},
  owner = {Stefan},
  timestamp = {2012.04.18},
  url = {http://www.springerlink.com/index/UL3V89802631X4P1.pdf}
}

@ARTICLE{Tsai2003,
  author = {Tsai, Richard and Osher, Stanley},
  title = {{Level Set Methods and their Applications in Image Science}},
  journal = {Communications in Mathematical Sciences},
  year = {2003},
  volume = {1},
  pages = {623--656},
  number = {4},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/segmentation/tsai2003.pdf:pdf},
  owner = {Stefan},
  timestamp = {2012.04.24},
  url = {http://projecteuclid.org/euclid.cms/1119655349}
}

@INPROCEEDINGS{Wang2011,
  author = {Wang, Yaping and Nie, Jingxin and Yap, PT and Shi, Feng and Guo,
	Lei and Shen, Dinggang},
  title = {{Robust Deformable-Surface-Based Skull-Stripping for Large-Scale
	Studies}},
  booktitle = {MICCAI - Medical Image Computing and Computer Assisted Interventions},
  year = {2011},
  editor = {Fichtinger, Gabor and Martel, Anne and Peters},
  pages = {635--642},
  address = {Toronto},
  publisher = {Springer LNCS},
  doi = {10.1007/978-3-642-23626-6\_78},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/wang2011.pdf:pdf},
  owner = {Stefan},
  timestamp = {2012.04.18},
  url = {http://www.springerlink.com/index/R772156766733370.pdf}
}

@ARTICLE{Zhuang2006,
  author = {Zhuang, A.H. and Valentino, D.J. and Toga, A.W.},
  title = {{Skull-stripping magnetic resonance brain images using a model-based
	level set}},
  journal = {NeuroImage},
  year = {2006},
  volume = {32},
  pages = {79--92},
  number = {1},
  doi = {10.1016/j.neuroimage.2006.03.019},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/skullStripping/zhuang06.pdf:pdf},
  issn = {1053-8119},
  owner = {Stefan},
  publisher = {Elsevier},
  timestamp = {2012.04.18},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811906002126}
}

@ARTICLE{Zitova2003,
  author = {Zitova, Barbara and Flusser, Jan},
  title = {{Image registration methods: a survey}},
  journal = {Image and Vision Computing},
  year = {2003},
  volume = {21},
  pages = {977--1000},
  number = {11},
  month = oct,
  doi = {10.1016/S0262-8856(03)00137-9},
  file = {:C$\backslash$:/Documents and Settings/Stefan/My Documents/Files/Literature/registration/zitova03.pdf:pdf},
  issn = {02628856},
  keywords = {feature detection,feature matching,image registration,mapping function,resampling},
  owner = {Stefan},
  timestamp = {2012.04.24},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0262885603001379}
}

